JorgeVB/code_unlearning_dataset · Datasets at Hugging Face

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Set the join clause for the query. def _set_join(self, query=None): """ Set the join clause for the query. """ if not query: query = self._query foreign_key = '%s.%s' % (self._related.get_table(), self._second_key) query.join(self._parent.get_table(), self....
Violinplots of the highest and lowest scoring of each modality def plot_best_worst_fits(assignments_df, data, modality_col='Modality', score='$\log_2 K$'): """Violinplots of the highest and lowest scoring of each modality""" ncols = 2 nrows = len(assignments_df.groupby(modality_col...
Wrapper around Seaborn's Violinplot specifically for [0, 1] ranged data What's different: - bw = 0.2: Sets bandwidth to be small and the same between datasets - scale = 'width': Sets the width of all violinplots to be the same - inner = None: Don't plot a boxplot or points inside the violinplot def vi...
Draw barplots grouped by modality of modality percentage per group Parameters ---------- Returns ------- Raises ------ def bar(self, counts, phenotype_to_color=None, ax=None, percentages=True): """Draw barplots grouped by modality of modality percentage per ...
Show the values underlying bayesian modality estimations of an event Parameters ---------- Returns ------- Raises ------ def event_estimation(self, event, logliks, logsumexps, renamed=''): """Show the values underlying bayesian modality estimations of an eve...
Assign the most likely modality given the fitted data Parameters ---------- fitted : pandas.DataFrame or pandas.Series Either a (n_modalities, features) DatFrame or (n_modalities,) Series, either of which will return the best modality for each feature. def p...
Calculate log-likelihood of a feature x for each model Converts all values that are exactly 1 or exactly 0 to 0.999 and 0.001 because they are out of range of the beta distribution. Parameters ---------- x : numpy.array-like A single vector to estimate the log-likel...
Convert floats to either integers or a nice looking fraction def nice_number_string(number, decimal_places=2): """Convert floats to either integers or a nice looking fraction""" if number == np.round(number): return str(int(number)) elif number < 1 and number > 0: invers...
Plot violins of each distribution in the model family Parameters ---------- n : int Number of random variables to generate kwargs : dict or keywords Any keyword arguments to seaborn.violinplot Returns ------- ax : matplotlib.Axes object ...
Get log-likelihood of models at each parameterization for given data Parameters ---------- feature : pandas.Series Percent-based values of a single feature. May contain NAs, but only non-NA values are used. Returns ------- logliks : pandas.DataFr...
Get the modality assignments of each splicing event in the data Parameters ---------- data : pandas.DataFrame A (n_samples, n_events) dataframe of splicing events' PSI scores. Must be psi scores which range from 0 to 1 Returns ------- log2_bayes_...
Guess the most likely modality for each event For each event that has at least one non-NA value, if no modalilites have logsumexp'd logliks greater than the log Bayes factor threshold, then they are assigned the 'multimodal' modality, because we cannot reject the null hypothesis that th...
Calculate log-likelihoods of each modality's parameterization Used for plotting the estimates of a single feature Parameters ---------- featre : pandas.Series A single feature's values. All values must range from 0 to 1. Returns ------- logliks : pa...
Get the log2 bayes factor of the fit for each modality def single_feature_fit(self, feature): """Get the log2 bayes factor of the fit for each modality""" if np.isfinite(feature).sum() == 0: series = pd.Series(index=MODALITY_ORDER) else: logbf_one_param = pd.Series( ...
r"""Visualize all modality family members with parameters Use violinplots to visualize distributions of modality family members Parameters ---------- n : int Number of random variables to generate kwargs : dict or keywords Any keyword arguments to seabor...
Given a list of bins, make a list of strings of those bin ranges Parameters ---------- bins : list_like List of anything, usually values of bin edges Returns ------- bin_ranges : list List of bin ranges >>> bin_range_strings((0, 0.5, 1)) ['0-0.5', '0.5-1'] def bin_ran...
Makes a histogram of each column the provided binsize Parameters ---------- data : pandas.DataFrame A samples x features dataframe. Each feature (column) will be binned into the provided bins bins : iterable Bins you would like to use for this data. Must include the final bin ...
Kullback-Leiber divergence of two probability distributions pandas dataframes, p and q Parameters ---------- p : pandas.DataFrame An nbins x features DataFrame, or (nbins,) Series q : pandas.DataFrame An nbins x features DataFrame, or (nbins,) Series Returns ------- kld...
Finds the per-column JSD between dataframes p and q Jensen-Shannon divergence of two probability distrubutions pandas dataframes, p and q. These distributions are usually created by running binify() on the dataframe. Parameters ---------- p : pandas.DataFrame An nbins x features DataFr...
Find the entropy of each column of a dataframe Parameters ---------- binned : pandas.DataFrame A nbins x features DataFrame of probability distributions, where each column sums to 1 base : numeric The log-base of the entropy. Default is 2, so the resulting entropy is in ...
Binify and calculate jensen-shannon divergence between two dataframes Parameters ---------- df1, df2 : pandas.DataFrames Dataframes to calculate JSD between columns of. Must have overlapping column names bins : array-like Bins to use for transforming df{1,2} into probability dis...
Jensen-Shannon divergence of features across phenotypes Parameters ---------- data : pandas.DataFrame A (n_samples, n_features) Dataframe groupby : mappable A samples to phenotypes mapping n_iter : int Number of bootstrap resampling iterations to perform for the with...
Transform a tall JSD dataframe to a square matrix of mean JSDs Parameters ---------- jsd_df : pandas.DataFrame A (n_features, n_phenotypes^2) dataframe of the JSD between each feature between and within phenotypes Returns ------- jsd_2d : pandas.DataFrame A (n_phenotype...
Start pcap's loop over the interface, calling the given callback for each packet :param callback: a function receiving (win_pcap, param, header, pkt_data) for each packet intercepted :param limit: how many packets to capture (A value of -1 or 0 is equivalent to infinity) def run(self, callback=None, li...
send a buffer as a packet to the network interface :param packet_buffer: buffer to send (length shouldn't exceed MAX_INT) def send(self, packet_buffer): """ send a buffer as a packet to the network interface :param packet_buffer: buffer to send (length shouldn't exceed MAX_INT) ...
:param pattern: a wildcard pattern to match the description of a network interface to capture packets on :param callback: a function to call with each intercepted packet def capture_on(pattern, callback): """ :param pattern: a wildcard pattern to match the description of a network interface to ...
:param device_name: the name (guid) of a device as provided by WinPcapDevices.list_devices() :param callback: a function to call with each intercepted packet def capture_on_device_name(device_name, callback): """ :param device_name: the name (guid) of a device as provided by WinPcapDevices.list...
Send a buffer as a packet to a network interface and optionally capture a response :param pattern: a wildcard pattern to match the description of a network interface to capture packets on :param packet_buffer: a buffer to send (length shouldn't exceed MAX_INT) :param callback: If not None, a fun...
Return the next value for a given sequence. def get_next_value( sequence_name='default', initial_value=1, reset_value=None, *, nowait=False, using=None): """ Return the next value for a given sequence. """ # Inner import because models cannot be imported before their application. f...
Check the status of all provided data and update the suite. def check(self, final_line_count): """Check the status of all provided data and update the suite.""" if self._lines_seen["version"]: self._process_version_lines() self._process_plan_lines(final_line_count)
Process version line rules. def _process_version_lines(self): """Process version line rules.""" if len(self._lines_seen["version"]) > 1: self._add_error(_("Multiple version lines appeared.")) elif self._lines_seen["version"][0] != 1: self._add_error(_("The version must b...
Process plan line rules. def _process_plan_lines(self, final_line_count): """Process plan line rules.""" if not self._lines_seen["plan"]: self._add_error(_("Missing a plan.")) return if len(self._lines_seen["plan"]) > 1: self._add_error(_("Only one plan line...
Check if a plan is on a valid line. def _plan_on_valid_line(self, at_line, final_line_count): """Check if a plan is on a valid line.""" # Put the common cases first. if at_line == 1 or at_line == final_line_count: return True # The plan may only appear on line 2 if the vers...
Handle a bail line. def handle_bail(self, bail): """Handle a bail line.""" self._add_error(_("Bailed: {reason}").format(reason=bail.reason))
Handle a plan that contains a SKIP directive. def handle_skipping_plan(self, skip_plan): """Handle a plan that contains a SKIP directive.""" skip_line = Result(True, None, skip_plan.directive.text, Directive("SKIP")) self._suite.addTest(Adapter(self._filename, skip_line))
Add an error test to the suite. def _add_error(self, message): """Add an error test to the suite.""" error_line = Result(False, None, message, Directive("")) self._suite.addTest(Adapter(self._filename, error_line))
Format an exception as diagnostics output. exception is the tuple as expected from sys.exc_info. def format_exception(exception): """Format an exception as diagnostics output. exception is the tuple as expected from sys.exc_info. """ exception_lines = traceback.format_exception(*exception) # ...
Generate tap.line.Line objects, given a file-like object `fh`. `fh` may be any object that implements both the iterator and context management protocol (i.e. it can be used in both a "with" statement and a "for...in" statement.) Trailing whitespace and newline characters will be automa...
Parse a line into whatever TAP category it belongs. def parse_line(self, text, fh=None): """Parse a line into whatever TAP category it belongs.""" match = self.ok.match(text) if match: return self._parse_result(True, match, fh) match = self.not_ok.match(text) if mat...
Parse a matching plan line. def _parse_plan(self, match): """Parse a matching plan line.""" expected_tests = int(match.group("expected")) directive = Directive(match.group("directive")) # Only SKIP directives are allowed in the plan. if directive.text and not directive.skip: ...
Parse a matching result line into a result instance. def _parse_result(self, ok, match, fh=None): """Parse a matching result line into a result instance.""" peek_match = None try: if fh is not None and self._try_peeking: peek_match = self.yaml_block_start.match(fh.pe...
Extract a raw yaml block from a file handler def _extract_yaml_block(self, indent, fh): """Extract a raw yaml block from a file handler""" raw_yaml = [] indent_match = re.compile(r"^{}".format(indent)) try: fh.next() while indent_match.match(fh.peek()): ...
Lazy load a yaml_block. If yaml support is not available, there is an error in parsing the yaml block, or no yaml is associated with this result, ``None`` will be returned. :rtype: dict def yaml_block(self): """Lazy load a yaml_block. If yaml support is not av...
Load any files found into a suite. Any directories are walked and their files are added as TAP files. :returns: A ``unittest.TestSuite`` instance def load(self, files): """Load any files found into a suite. Any directories are walked and their files are added as TAP files. :...
Load a test suite with test lines from the provided TAP file. :returns: A ``unittest.TestSuite`` instance def load_suite_from_file(self, filename): """Load a test suite with test lines from the provided TAP file. :returns: A ``unittest.TestSuite`` instance """ suite = unittest...
Load a test suite with test lines from the TAP stream on STDIN. :returns: A ``unittest.TestSuite`` instance def load_suite_from_stdin(self): """Load a test suite with test lines from the TAP stream on STDIN. :returns: A ``unittest.TestSuite`` instance """ suite = unittest.Test...
Load a suite with lines produced by the line generator. def _load_lines(self, filename, line_generator, suite, rules): """Load a suite with lines produced by the line generator.""" line_counter = 0 for line in line_generator: line_counter += 1 if line.category in self.i...
Keep track of which test cases have executed. def _track(self, class_name): """Keep track of which test cases have executed.""" if self._test_cases.get(class_name) is None: if self.streaming and self.header: self._write_test_case_header(class_name, self.stream) ...
Notify the tracker how many total tests there will be. def set_plan(self, total): """Notify the tracker how many total tests there will be.""" self.plan = total if self.streaming: # This will only write the plan if we haven't written it # already but we want to check if ...
Generate TAP reports. The results are either combined into a single output file or the output file name is generated from the test case. def generate_tap_reports(self): """Generate TAP reports. The results are either combined into a single output file or the output file name i...
Write the plan line to the stream. If we have a plan and have not yet written it out, write it to the given stream. def _write_plan(self, stream): """Write the plan line to the stream. If we have a plan and have not yet written it out, write it to the given stream. """...
Get the TAP output file path for the test case. def _get_tap_file_path(self, test_case): """Get the TAP output file path for the test case.""" sanitized_test_case = test_case.translate(self._sanitized_table) tap_file = sanitized_test_case + ".tap" if self.outdir: return os.p...
Entry point for ``tappy`` command. def main(argv=sys.argv, stream=sys.stderr): """Entry point for ``tappy`` command.""" args = parse_args(argv) suite = build_suite(args) runner = unittest.TextTestRunner(verbosity=args.verbose, stream=stream) result = runner.run(suite) return get_status(result)
Build a test suite by loading TAP files or a TAP stream. def build_suite(args): """Build a test suite by loading TAP files or a TAP stream.""" loader = Loader() if len(args.files) == 0 or args.files[0] == "-": suite = loader.load_suite_from_stdin() else: suite = loader.load(args.files) ...
Add a failure to the result. def addFailure(self, result): """Add a failure to the result.""" result.addFailure(self, (Exception, Exception(), None)) # Since TAP will not provide assertion data, clean up the assertion # section so it is not so spaced out. test, err = result.fail...
Based on example https://bitbucket.org/zzzeek/sqlalchemy/src/73095b353124/examples/nested_sets/nested_sets.py?at=master def mptt_before_insert(mapper, connection, instance): """ Based on example https://bitbucket.org/zzzeek/sqlalchemy/src/73095b353124/examples/nested_sets/nested_sets.py?at=master """ ...
Based on this example: http://stackoverflow.com/questions/889527/move-node-in-nested-set def mptt_before_update(mapper, connection, instance): """ Based on this example: http://stackoverflow.com/questions/889527/move-node-in-nested-set """ node_id = getattr(instance, instance.get_pk_name())...
Event listener to recursively expire `left` and `right` attributes the parents of all modified instances part of this flush. def after_flush_postexec(self, session, context): """ Event listener to recursively expire `left` and `right` attributes the parents of all modified instances par...
class or instance level method which returns True if self is ancestor (closer to root) of other else False. Optional flag `inclusive` on whether or not to treat self as ancestor of self. For example see: * :mod:`sqlalchemy_mptt.tests.cases.integrity.test_hierarchy_structure` def is_an...
Moving one node of tree inside another For example see: * :mod:`sqlalchemy_mptt.tests.cases.move_node.test_move_inside_function` * :mod:`sqlalchemy_mptt.tests.cases.move_node.test_move_inside_to_the_same_parent_function` def move_inside(self, parent_id): """ Moving one node of tree in...
Moving one node of tree after another For example see :mod:`sqlalchemy_mptt.tests.cases.move_node.test_move_after_function` def move_after(self, node_id): """ Moving one node of tree after another For example see :mod:`sqlalchemy_mptt.tests.cases.move_node.test_move_after_function` ""...
Moving one node of tree before another For example see: * :mod:`sqlalchemy_mptt.tests.cases.move_node.test_move_before_function` * :mod:`sqlalchemy_mptt.tests.cases.move_node.test_move_before_to_other_tree` * :mod:`sqlalchemy_mptt.tests.cases.move_node.test_move_before_to_top_level` d...
Node to the left of the current node at the same level For example see :mod:`sqlalchemy_mptt.tests.cases.get_tree.test_leftsibling_in_level` def leftsibling_in_level(self): """ Node to the left of the current node at the same level For example see :mod:`sqlalchemy_mptt.tests.c...
Helper method for ``get_tree``. def _node_to_dict(cls, node, json, json_fields): """ Helper method for ``get_tree``. """ if json: pk_name = node.get_pk_name() # jqTree or jsTree format result = {'id': getattr(node, pk_name), 'label': node.__repr__()} ...
This method generate tree of current node table in dict or json format. You can make custom query with attribute ``query``. By default it return all nodes in table. Args: session (:mod:`sqlalchemy.orm.session.Session`): SQLAlchemy session Kwargs: json (bool): if...
This method generate a branch from a tree, begining with current node. For example: node7.drilldown_tree() .. code:: level Nested sets example 1 1(1)22 --------------------- ___________...
Generate path from a leaf or intermediate node to the root. For example: node11.path_to_root() .. code:: level Nested sets example ----------------------------------------- 1 \| 1(1)22 ...
This method rebuid tree. Args: session (:mod:`sqlalchemy.orm.session.Session`): SQLAlchemy session tree_id (int or str): id of tree Example: * :mod:`sqlalchemy_mptt.tests.cases.get_tree.test_rebuild` def rebuild_tree(cls, session, tree_id): """ This method reb...
This function rebuid tree. Args: session (:mod:`sqlalchemy.orm.session.Session`): SQLAlchemy session Kwargs: tree_id (int or str): id of tree, default None Example: * :mod:`sqlalchemy_mptt.tests.TestTree.test_rebuild` def rebuild(cls, session, tree_id=None): ...
qx: Returns the probability that a life aged x dies before 1 year With the convention: the true probability is qx/1000 Args: mt: the mortality table x: the age as integer number. def qx(mt, x): """ qx: Returns the probability that a life aged x dies before 1 year With th...
lx : Returns the number of survivors at begining of age x def lx(mt, x): """ lx : Returns the number of survivors at begining of age x """ if x < len(mt.lx): return mt.lx[x] else: return 0
Returns the number of dying at begining of age x def dx(mt, x): """ Returns the number of dying at begining of age x """ end_x_val = mt.lx.index(0) if x < end_x_val: return mt.lx[x] - mt.lx[x + 1] else: return 0.0
tpx : Returns the probability that x will survive within t years def tpx(mt, x, t): """ tpx : Returns the probability that x will survive within t years """ """ npx : Returns n years survival probability at age x """ return mt.lx[x + t] / mt.lx[x]
nqx : Returns the probability to die within n years at age x def tqx(mt, x, t): """ nqx : Returns the probability to die within n years at age x """ return (mt.lx[x] - mt.lx[x + t]) / mt.lx[x]
n/qx : Probability to die in n years being alive at age x. Probability that x survives n year, and then dies in th subsequent t years def tqxn(mt, x, n, t): """ n/qx : Probability to die in n years being alive at age x. Probability that x survives n year, and then dies in th subsequent t years """ retu...
ex : Returns the curtate expectation of life. Life expectancy def ex(mt, x): """ ex : Returns the curtate expectation of life. Life expectancy """ sum1 = 0 for j in mt.lx[x + 1:-1]: sum1 += j #print sum1 try: return sum1 / mt.lx[x] + 0.5 except: return 0
Return the Sx def Sx(mt, x): """ Return the Sx """ n = len(mt.Nx) sum1 = 0 for j in range(x, n): k = mt.Nx[j] sum1 += k return sum1
Return the Cx def Cx(mt, x): """ Return the Cx """ return ((1 / (1 + mt.i)) ** (x + 1)) * mt.dx[x] * ((1 + mt.i) ** 0.5)
Return the Mx def Mx(mt, x): """ Return the Mx """ n = len(mt.Cx) sum1 = 0 for j in range(x, n): k = mt.Cx[j] sum1 += k return sum1
nEx : Returns the EPV of a pure endowment (deferred capital). Pure endowment benefits are conditional on the survival of the policyholder. (v^n * npx) def nEx(mt, x, n): """ nEx : Returns the EPV of a pure endowment (deferred capital). Pure endowment benefits are conditional on the survival of the policy...
(A^1)x:n : Returns the EPV (net single premium) of a term insurance. def Axn(mt, x, n): """ (A^1)x:n : Returns the EPV (net single premium) of a term insurance. """ return (mt.Mx[x] - mt.Mx[x + n]) / mt.Dx[x]
AExn : Returns the EPV of a endowment insurance. An endowment insurance provides a combination of a term insurance and a pure endowment def AExn(mt, x, n): """ AExn : Returns the EPV of a endowment insurance. An endowment insurance provides a combination of a term insurance and a pure endowment """ ...
n/Ax : Returns the EPV (net single premium) of a deferred whole life insurance. def tAx(mt, x, t): """ n/Ax : Returns the EPV (net single premium) of a deferred whole life insurance. """ return mt.Mx[x + t] / mt.Dx[x]
This function evaluates the APV of a geometrically increasing annual annuity-due def qAx(mt, x, q): """ This function evaluates the APV of a geometrically increasing annual annuity-due """ q = float(q) j = (mt.i - q) / (1 + q) mtj = Actuarial(nt=mt.nt, i=j) return Ax(mtj, x)
äxn : Return the actuarial present value of a (immediate) temporal (term certain) annuity: n-year temporary life annuity-anticipatory. Payable 'm' per year at the beginning of the period def aaxn(mt, x, n, m=1): """ äxn : Return the actuarial present value of a (immediate) temporal (term certain) annuity: ...
äx : Returns the actuarial present value of an (immediate) annuity of 1 per time period (whole life annuity-anticipatory). Payable 'm' per year at the beginning of the period def aax(mt, x, m=1): """ äx : Returns the actuarial present value of an (immediate) annuity of 1 per time period (whole life annui...
ax : Returns the actuarial present value of an (immediate) annuity of 1 per time period (whole life annuity-late). Payable 'm' per year at the ends of the period def ax(mt, x, m=1): """ ax : Returns the actuarial present value of an (immediate) annuity of 1 per time period (whole life annuity-late). Paya...
n/äx : Return the actuarial present value of a deferred annuity (deferred n years): n-year deferred whole life annuity-anticipatory. Payable 'm' per year at the beginning of the period def taax(mt, x, t, m=1): """ n/äx : Return the actuarial present value of a deferred annuity (deferred n years): n-year ...
during a term certain, IAn def Iaaxn(mt, x, n, args): """ during a term certain, IAn """ return (Sx(mt, x) - Sx(nt, x + n) - n Nx(nt, x + n)) / Dx(nt, x)
during a term certain, IAn def Iaxn(mt, x, n, args): """ during a term certain, IAn """ return (Sx(mt, x + 1) - Sx(mt, x + n + 1) - n Nx(mt, x + n + 1)) / Dx(mt, x)
(Iä)x : Returns the present value of annuity-certain at the beginning of the first year and increasing linerly. Arithmetically increasing annuity-anticipatory def Iaax(mt, x, *args): """ (Iä)x : Returns the present value of annuity-certain at the beginning of the first year and increasing linerly. Arithm...
(Ia)x : Returns the present value of annuity-certain at the end of the first year and increasing linerly. Arithmetically increasing annuity-late def Iax(mt, x, *args): """ (Ia)x : Returns the present value of annuity-certain at the end of the first year and increasing linerly. Arithmetically increasing a...
deffered t years def Itaax(mt, x, t): """ deffered t years """ return (Sx(mt, x) - Sx(mt, x + t)) / Dx(mt, x)
deffered t years def Itax(mt, x, t): """ deffered t years """ return (Sx(mt, x + 1) - Sx(mt, x + t + 1)) / Dx(mt, x)
geometrica def qax(mt, x, q, m=1): """ geometrica """ q = float(q) j = (mt.i - q) / (1 + q) mtj = Actuarial(nt=mt.nt, i=j) return ax(mtj, x, m)
geometrica def qaax(mt, x, q, m=1): """ geometrica """ q = float(q) j = (mt.i - q) / (1 + q) mtj = Actuarial(nt=mt.nt, i=j) return aax(mtj, x, m)
geometrica def qaxn(mt, x, n, q, m=1): """ geometrica """ q = float(q) j = (mt.i - q) / (1 + q) mtj = Actuarial(nt=mt.nt, i=j) return axn(mtj, x, n, m)
geometrica def qaaxn(mt, x, n, q, m = 1): """ geometrica """ #i = float(nt[1]) q = float(q) j = (mt.i - q) / (1 + q) mtj = Actuarial(nt=mt.nt, i=j) return aaxn(mtj, x, n, m)
geometrica def qtax(mt, x, t, q, m=1): """ geometrica """ q = float(q) j = (mt.i - q) / (1 + q) mtj = Actuarial(nt=mt.nt, i=j) return tax(mtj, x, t) + ((float(m - 1) / float(m * 2)) * (1 - nEx(mt, x, t)))
geometrica def qtaax(mt, x, t, q, m=1): """ geometrica """ q = float(q) j = (mt.i - q) / (1 + q) mtj = Actuarial(nt=mt.nt, i=j) return taax(mtj, x, t) - ((float(m - 1) / float(m * 2)) * (1 - nEx(mt, x, t)))

Set the join clause for the query. def _set_join(self, query=None): """ Set the join clause for the query. """ if not query: query = self._query foreign_key = '%s.%s' % (self._related.get_table(), self._second_key) query.join(self._parent.get_table(), self....

Violinplots of the highest and lowest scoring of each modality def plot_best_worst_fits(assignments_df, data, modality_col='Modality', score='$\log_2 K$'): """Violinplots of the highest and lowest scoring of each modality""" ncols = 2 nrows = len(assignments_df.groupby(modality_col...

Wrapper around Seaborn's Violinplot specifically for [0, 1] ranged data What's different: - bw = 0.2: Sets bandwidth to be small and the same between datasets - scale = 'width': Sets the width of all violinplots to be the same - inner = None: Don't plot a boxplot or points inside the violinplot def vi...

Draw barplots grouped by modality of modality percentage per group Parameters ---------- Returns ------- Raises ------ def bar(self, counts, phenotype_to_color=None, ax=None, percentages=True): """Draw barplots grouped by modality of modality percentage per ...

Show the values underlying bayesian modality estimations of an event Parameters ---------- Returns ------- Raises ------ def event_estimation(self, event, logliks, logsumexps, renamed=''): """Show the values underlying bayesian modality estimations of an eve...

Assign the most likely modality given the fitted data Parameters ---------- fitted : pandas.DataFrame or pandas.Series Either a (n_modalities, features) DatFrame or (n_modalities,) Series, either of which will return the best modality for each feature. def p...

Calculate log-likelihood of a feature x for each model Converts all values that are exactly 1 or exactly 0 to 0.999 and 0.001 because they are out of range of the beta distribution. Parameters ---------- x : numpy.array-like A single vector to estimate the log-likel...

Convert floats to either integers or a nice looking fraction def nice_number_string(number, decimal_places=2): """Convert floats to either integers or a nice looking fraction""" if number == np.round(number): return str(int(number)) elif number < 1 and number > 0: invers...

Plot violins of each distribution in the model family Parameters ---------- n : int Number of random variables to generate kwargs : dict or keywords Any keyword arguments to seaborn.violinplot Returns ------- ax : matplotlib.Axes object ...

Get log-likelihood of models at each parameterization for given data Parameters ---------- feature : pandas.Series Percent-based values of a single feature. May contain NAs, but only non-NA values are used. Returns ------- logliks : pandas.DataFr...

Get the modality assignments of each splicing event in the data Parameters ---------- data : pandas.DataFrame A (n_samples, n_events) dataframe of splicing events' PSI scores. Must be psi scores which range from 0 to 1 Returns ------- log2_bayes_...

Guess the most likely modality for each event For each event that has at least one non-NA value, if no modalilites have logsumexp'd logliks greater than the log Bayes factor threshold, then they are assigned the 'multimodal' modality, because we cannot reject the null hypothesis that th...

Calculate log-likelihoods of each modality's parameterization Used for plotting the estimates of a single feature Parameters ---------- featre : pandas.Series A single feature's values. All values must range from 0 to 1. Returns ------- logliks : pa...

Get the log2 bayes factor of the fit for each modality def single_feature_fit(self, feature): """Get the log2 bayes factor of the fit for each modality""" if np.isfinite(feature).sum() == 0: series = pd.Series(index=MODALITY_ORDER) else: logbf_one_param = pd.Series( ...

r"""Visualize all modality family members with parameters Use violinplots to visualize distributions of modality family members Parameters ---------- n : int Number of random variables to generate kwargs : dict or keywords Any keyword arguments to seabor...

Given a list of bins, make a list of strings of those bin ranges Parameters ---------- bins : list_like List of anything, usually values of bin edges Returns ------- bin_ranges : list List of bin ranges >>> bin_range_strings((0, 0.5, 1)) ['0-0.5', '0.5-1'] def bin_ran...

Makes a histogram of each column the provided binsize Parameters ---------- data : pandas.DataFrame A samples x features dataframe. Each feature (column) will be binned into the provided bins bins : iterable Bins you would like to use for this data. Must include the final bin ...

Kullback-Leiber divergence of two probability distributions pandas dataframes, p and q Parameters ---------- p : pandas.DataFrame An nbins x features DataFrame, or (nbins,) Series q : pandas.DataFrame An nbins x features DataFrame, or (nbins,) Series Returns ------- kld...

Finds the per-column JSD between dataframes p and q Jensen-Shannon divergence of two probability distrubutions pandas dataframes, p and q. These distributions are usually created by running binify() on the dataframe. Parameters ---------- p : pandas.DataFrame An nbins x features DataFr...

Find the entropy of each column of a dataframe Parameters ---------- binned : pandas.DataFrame A nbins x features DataFrame of probability distributions, where each column sums to 1 base : numeric The log-base of the entropy. Default is 2, so the resulting entropy is in ...

Binify and calculate jensen-shannon divergence between two dataframes Parameters ---------- df1, df2 : pandas.DataFrames Dataframes to calculate JSD between columns of. Must have overlapping column names bins : array-like Bins to use for transforming df{1,2} into probability dis...

Jensen-Shannon divergence of features across phenotypes Parameters ---------- data : pandas.DataFrame A (n_samples, n_features) Dataframe groupby : mappable A samples to phenotypes mapping n_iter : int Number of bootstrap resampling iterations to perform for the with...

Transform a tall JSD dataframe to a square matrix of mean JSDs Parameters ---------- jsd_df : pandas.DataFrame A (n_features, n_phenotypes^2) dataframe of the JSD between each feature between and within phenotypes Returns ------- jsd_2d : pandas.DataFrame A (n_phenotype...

Start pcap's loop over the interface, calling the given callback for each packet :param callback: a function receiving (win_pcap, param, header, pkt_data) for each packet intercepted :param limit: how many packets to capture (A value of -1 or 0 is equivalent to infinity) def run(self, callback=None, li...

send a buffer as a packet to the network interface :param packet_buffer: buffer to send (length shouldn't exceed MAX_INT) def send(self, packet_buffer): """ send a buffer as a packet to the network interface :param packet_buffer: buffer to send (length shouldn't exceed MAX_INT) ...

:param pattern: a wildcard pattern to match the description of a network interface to capture packets on :param callback: a function to call with each intercepted packet def capture_on(pattern, callback): """ :param pattern: a wildcard pattern to match the description of a network interface to ...

:param device_name: the name (guid) of a device as provided by WinPcapDevices.list_devices() :param callback: a function to call with each intercepted packet def capture_on_device_name(device_name, callback): """ :param device_name: the name (guid) of a device as provided by WinPcapDevices.list...

Send a buffer as a packet to a network interface and optionally capture a response :param pattern: a wildcard pattern to match the description of a network interface to capture packets on :param packet_buffer: a buffer to send (length shouldn't exceed MAX_INT) :param callback: If not None, a fun...

Return the next value for a given sequence. def get_next_value( sequence_name='default', initial_value=1, reset_value=None, *, nowait=False, using=None): """ Return the next value for a given sequence. """ # Inner import because models cannot be imported before their application. f...

Check the status of all provided data and update the suite. def check(self, final_line_count): """Check the status of all provided data and update the suite.""" if self._lines_seen["version"]: self._process_version_lines() self._process_plan_lines(final_line_count)

Process version line rules. def _process_version_lines(self): """Process version line rules.""" if len(self._lines_seen["version"]) > 1: self._add_error(_("Multiple version lines appeared.")) elif self._lines_seen["version"][0] != 1: self._add_error(_("The version must b...

Process plan line rules. def _process_plan_lines(self, final_line_count): """Process plan line rules.""" if not self._lines_seen["plan"]: self._add_error(_("Missing a plan.")) return if len(self._lines_seen["plan"]) > 1: self._add_error(_("Only one plan line...

Check if a plan is on a valid line. def _plan_on_valid_line(self, at_line, final_line_count): """Check if a plan is on a valid line.""" # Put the common cases first. if at_line == 1 or at_line == final_line_count: return True # The plan may only appear on line 2 if the vers...

Handle a bail line. def handle_bail(self, bail): """Handle a bail line.""" self._add_error(_("Bailed: {reason}").format(reason=bail.reason))

Handle a plan that contains a SKIP directive. def handle_skipping_plan(self, skip_plan): """Handle a plan that contains a SKIP directive.""" skip_line = Result(True, None, skip_plan.directive.text, Directive("SKIP")) self._suite.addTest(Adapter(self._filename, skip_line))

Add an error test to the suite. def _add_error(self, message): """Add an error test to the suite.""" error_line = Result(False, None, message, Directive("")) self._suite.addTest(Adapter(self._filename, error_line))

Format an exception as diagnostics output. exception is the tuple as expected from sys.exc_info. def format_exception(exception): """Format an exception as diagnostics output. exception is the tuple as expected from sys.exc_info. """ exception_lines = traceback.format_exception(*exception) # ...

Generate tap.line.Line objects, given a file-like object `fh`. `fh` may be any object that implements both the iterator and context management protocol (i.e. it can be used in both a "with" statement and a "for...in" statement.) Trailing whitespace and newline characters will be automa...

Parse a line into whatever TAP category it belongs. def parse_line(self, text, fh=None): """Parse a line into whatever TAP category it belongs.""" match = self.ok.match(text) if match: return self._parse_result(True, match, fh) match = self.not_ok.match(text) if mat...

Parse a matching plan line. def _parse_plan(self, match): """Parse a matching plan line.""" expected_tests = int(match.group("expected")) directive = Directive(match.group("directive")) # Only SKIP directives are allowed in the plan. if directive.text and not directive.skip: ...

Parse a matching result line into a result instance. def _parse_result(self, ok, match, fh=None): """Parse a matching result line into a result instance.""" peek_match = None try: if fh is not None and self._try_peeking: peek_match = self.yaml_block_start.match(fh.pe...

Extract a raw yaml block from a file handler def _extract_yaml_block(self, indent, fh): """Extract a raw yaml block from a file handler""" raw_yaml = [] indent_match = re.compile(r"^{}".format(indent)) try: fh.next() while indent_match.match(fh.peek()): ...

Lazy load a yaml_block. If yaml support is not available, there is an error in parsing the yaml block, or no yaml is associated with this result, ``None`` will be returned. :rtype: dict def yaml_block(self): """Lazy load a yaml_block. If yaml support is not av...

Load any files found into a suite. Any directories are walked and their files are added as TAP files. :returns: A ``unittest.TestSuite`` instance def load(self, files): """Load any files found into a suite. Any directories are walked and their files are added as TAP files. :...

Load a test suite with test lines from the provided TAP file. :returns: A ``unittest.TestSuite`` instance def load_suite_from_file(self, filename): """Load a test suite with test lines from the provided TAP file. :returns: A ``unittest.TestSuite`` instance """ suite = unittest...

Load a test suite with test lines from the TAP stream on STDIN. :returns: A ``unittest.TestSuite`` instance def load_suite_from_stdin(self): """Load a test suite with test lines from the TAP stream on STDIN. :returns: A ``unittest.TestSuite`` instance """ suite = unittest.Test...

Load a suite with lines produced by the line generator. def _load_lines(self, filename, line_generator, suite, rules): """Load a suite with lines produced by the line generator.""" line_counter = 0 for line in line_generator: line_counter += 1 if line.category in self.i...

Keep track of which test cases have executed. def _track(self, class_name): """Keep track of which test cases have executed.""" if self._test_cases.get(class_name) is None: if self.streaming and self.header: self._write_test_case_header(class_name, self.stream) ...

Notify the tracker how many total tests there will be. def set_plan(self, total): """Notify the tracker how many total tests there will be.""" self.plan = total if self.streaming: # This will only write the plan if we haven't written it # already but we want to check if ...

Generate TAP reports. The results are either combined into a single output file or the output file name is generated from the test case. def generate_tap_reports(self): """Generate TAP reports. The results are either combined into a single output file or the output file name i...

Write the plan line to the stream. If we have a plan and have not yet written it out, write it to the given stream. def _write_plan(self, stream): """Write the plan line to the stream. If we have a plan and have not yet written it out, write it to the given stream. """...

Get the TAP output file path for the test case. def _get_tap_file_path(self, test_case): """Get the TAP output file path for the test case.""" sanitized_test_case = test_case.translate(self._sanitized_table) tap_file = sanitized_test_case + ".tap" if self.outdir: return os.p...

Entry point for ``tappy`` command. def main(argv=sys.argv, stream=sys.stderr): """Entry point for ``tappy`` command.""" args = parse_args(argv) suite = build_suite(args) runner = unittest.TextTestRunner(verbosity=args.verbose, stream=stream) result = runner.run(suite) return get_status(result)

Build a test suite by loading TAP files or a TAP stream. def build_suite(args): """Build a test suite by loading TAP files or a TAP stream.""" loader = Loader() if len(args.files) == 0 or args.files[0] == "-": suite = loader.load_suite_from_stdin() else: suite = loader.load(args.files) ...

Add a failure to the result. def addFailure(self, result): """Add a failure to the result.""" result.addFailure(self, (Exception, Exception(), None)) # Since TAP will not provide assertion data, clean up the assertion # section so it is not so spaced out. test, err = result.fail...

Based on example https://bitbucket.org/zzzeek/sqlalchemy/src/73095b353124/examples/nested_sets/nested_sets.py?at=master def mptt_before_insert(mapper, connection, instance): """ Based on example https://bitbucket.org/zzzeek/sqlalchemy/src/73095b353124/examples/nested_sets/nested_sets.py?at=master """ ...

Based on this example: http://stackoverflow.com/questions/889527/move-node-in-nested-set def mptt_before_update(mapper, connection, instance): """ Based on this example: http://stackoverflow.com/questions/889527/move-node-in-nested-set """ node_id = getattr(instance, instance.get_pk_name())...

Event listener to recursively expire `left` and `right` attributes the parents of all modified instances part of this flush. def after_flush_postexec(self, session, context): """ Event listener to recursively expire `left` and `right` attributes the parents of all modified instances par...

class or instance level method which returns True if self is ancestor (closer to root) of other else False. Optional flag `inclusive` on whether or not to treat self as ancestor of self. For example see: * :mod:`sqlalchemy_mptt.tests.cases.integrity.test_hierarchy_structure` def is_an...

Moving one node of tree inside another For example see: * :mod:`sqlalchemy_mptt.tests.cases.move_node.test_move_inside_function` * :mod:`sqlalchemy_mptt.tests.cases.move_node.test_move_inside_to_the_same_parent_function` def move_inside(self, parent_id): """ Moving one node of tree in...

Moving one node of tree after another For example see :mod:`sqlalchemy_mptt.tests.cases.move_node.test_move_after_function` def move_after(self, node_id): """ Moving one node of tree after another For example see :mod:`sqlalchemy_mptt.tests.cases.move_node.test_move_after_function` ""...

Moving one node of tree before another For example see: * :mod:`sqlalchemy_mptt.tests.cases.move_node.test_move_before_function` * :mod:`sqlalchemy_mptt.tests.cases.move_node.test_move_before_to_other_tree` * :mod:`sqlalchemy_mptt.tests.cases.move_node.test_move_before_to_top_level` d...

Node to the left of the current node at the same level For example see :mod:`sqlalchemy_mptt.tests.cases.get_tree.test_leftsibling_in_level` def leftsibling_in_level(self): """ Node to the left of the current node at the same level For example see :mod:`sqlalchemy_mptt.tests.c...

Helper method for ``get_tree``. def _node_to_dict(cls, node, json, json_fields): """ Helper method for ``get_tree``. """ if json: pk_name = node.get_pk_name() # jqTree or jsTree format result = {'id': getattr(node, pk_name), 'label': node.__repr__()} ...

This method generate tree of current node table in dict or json format. You can make custom query with attribute ``query``. By default it return all nodes in table. Args: session (:mod:`sqlalchemy.orm.session.Session`): SQLAlchemy session Kwargs: json (bool): if...

This method generate a branch from a tree, begining with current node. For example: node7.drilldown_tree() .. code:: level Nested sets example 1 1(1)22 --------------------- ___________...

Generate path from a leaf or intermediate node to the root. For example: node11.path_to_root() .. code:: level Nested sets example ----------------------------------------- 1 | 1(1)22 ...

This method rebuid tree. Args: session (:mod:`sqlalchemy.orm.session.Session`): SQLAlchemy session tree_id (int or str): id of tree Example: * :mod:`sqlalchemy_mptt.tests.cases.get_tree.test_rebuild` def rebuild_tree(cls, session, tree_id): """ This method reb...

This function rebuid tree. Args: session (:mod:`sqlalchemy.orm.session.Session`): SQLAlchemy session Kwargs: tree_id (int or str): id of tree, default None Example: * :mod:`sqlalchemy_mptt.tests.TestTree.test_rebuild` def rebuild(cls, session, tree_id=None): ...

qx: Returns the probability that a life aged x dies before 1 year With the convention: the true probability is qx/1000 Args: mt: the mortality table x: the age as integer number. def qx(mt, x): """ qx: Returns the probability that a life aged x dies before 1 year With th...

lx : Returns the number of survivors at begining of age x def lx(mt, x): """ lx : Returns the number of survivors at begining of age x """ if x < len(mt.lx): return mt.lx[x] else: return 0

Returns the number of dying at begining of age x def dx(mt, x): """ Returns the number of dying at begining of age x """ end_x_val = mt.lx.index(0) if x < end_x_val: return mt.lx[x] - mt.lx[x + 1] else: return 0.0

tpx : Returns the probability that x will survive within t years def tpx(mt, x, t): """ tpx : Returns the probability that x will survive within t years """ """ npx : Returns n years survival probability at age x """ return mt.lx[x + t] / mt.lx[x]

nqx : Returns the probability to die within n years at age x def tqx(mt, x, t): """ nqx : Returns the probability to die within n years at age x """ return (mt.lx[x] - mt.lx[x + t]) / mt.lx[x]

n/qx : Probability to die in n years being alive at age x. Probability that x survives n year, and then dies in th subsequent t years def tqxn(mt, x, n, t): """ n/qx : Probability to die in n years being alive at age x. Probability that x survives n year, and then dies in th subsequent t years """ retu...

ex : Returns the curtate expectation of life. Life expectancy def ex(mt, x): """ ex : Returns the curtate expectation of life. Life expectancy """ sum1 = 0 for j in mt.lx[x + 1:-1]: sum1 += j #print sum1 try: return sum1 / mt.lx[x] + 0.5 except: return 0

Return the Sx def Sx(mt, x): """ Return the Sx """ n = len(mt.Nx) sum1 = 0 for j in range(x, n): k = mt.Nx[j] sum1 += k return sum1

Return the Cx def Cx(mt, x): """ Return the Cx """ return ((1 / (1 + mt.i)) ** (x + 1)) * mt.dx[x] * ((1 + mt.i) ** 0.5)

Return the Mx def Mx(mt, x): """ Return the Mx """ n = len(mt.Cx) sum1 = 0 for j in range(x, n): k = mt.Cx[j] sum1 += k return sum1

nEx : Returns the EPV of a pure endowment (deferred capital). Pure endowment benefits are conditional on the survival of the policyholder. (v^n * npx) def nEx(mt, x, n): """ nEx : Returns the EPV of a pure endowment (deferred capital). Pure endowment benefits are conditional on the survival of the policy...

(A^1)x:n : Returns the EPV (net single premium) of a term insurance. def Axn(mt, x, n): """ (A^1)x:n : Returns the EPV (net single premium) of a term insurance. """ return (mt.Mx[x] - mt.Mx[x + n]) / mt.Dx[x]

AExn : Returns the EPV of a endowment insurance. An endowment insurance provides a combination of a term insurance and a pure endowment def AExn(mt, x, n): """ AExn : Returns the EPV of a endowment insurance. An endowment insurance provides a combination of a term insurance and a pure endowment """ ...

n/Ax : Returns the EPV (net single premium) of a deferred whole life insurance. def tAx(mt, x, t): """ n/Ax : Returns the EPV (net single premium) of a deferred whole life insurance. """ return mt.Mx[x + t] / mt.Dx[x]

This function evaluates the APV of a geometrically increasing annual annuity-due def qAx(mt, x, q): """ This function evaluates the APV of a geometrically increasing annual annuity-due """ q = float(q) j = (mt.i - q) / (1 + q) mtj = Actuarial(nt=mt.nt, i=j) return Ax(mtj, x)

äxn : Return the actuarial present value of a (immediate) temporal (term certain) annuity: n-year temporary life annuity-anticipatory. Payable 'm' per year at the beginning of the period def aaxn(mt, x, n, m=1): """ äxn : Return the actuarial present value of a (immediate) temporal (term certain) annuity: ...

äx : Returns the actuarial present value of an (immediate) annuity of 1 per time period (whole life annuity-anticipatory). Payable 'm' per year at the beginning of the period def aax(mt, x, m=1): """ äx : Returns the actuarial present value of an (immediate) annuity of 1 per time period (whole life annui...

ax : Returns the actuarial present value of an (immediate) annuity of 1 per time period (whole life annuity-late). Payable 'm' per year at the ends of the period def ax(mt, x, m=1): """ ax : Returns the actuarial present value of an (immediate) annuity of 1 per time period (whole life annuity-late). Paya...

n/äx : Return the actuarial present value of a deferred annuity (deferred n years): n-year deferred whole life annuity-anticipatory. Payable 'm' per year at the beginning of the period def taax(mt, x, t, m=1): """ n/äx : Return the actuarial present value of a deferred annuity (deferred n years): n-year ...

during a term certain, IAn def Iaaxn(mt, x, n, *args): """ during a term certain, IAn """ return (Sx(mt, x) - Sx(nt, x + n) - n * Nx(nt, x + n)) / Dx(nt, x)

during a term certain, IAn def Iaxn(mt, x, n, *args): """ during a term certain, IAn """ return (Sx(mt, x + 1) - Sx(mt, x + n + 1) - n * Nx(mt, x + n + 1)) / Dx(mt, x)

(Iä)x : Returns the present value of annuity-certain at the beginning of the first year and increasing linerly. Arithmetically increasing annuity-anticipatory def Iaax(mt, x, *args): """ (Iä)x : Returns the present value of annuity-certain at the beginning of the first year and increasing linerly. Arithm...

(Ia)x : Returns the present value of annuity-certain at the end of the first year and increasing linerly. Arithmetically increasing annuity-late def Iax(mt, x, *args): """ (Ia)x : Returns the present value of annuity-certain at the end of the first year and increasing linerly. Arithmetically increasing a...

deffered t years def Itaax(mt, x, t): """ deffered t years """ return (Sx(mt, x) - Sx(mt, x + t)) / Dx(mt, x)

deffered t years def Itax(mt, x, t): """ deffered t years """ return (Sx(mt, x + 1) - Sx(mt, x + t + 1)) / Dx(mt, x)

geometrica def qax(mt, x, q, m=1): """ geometrica """ q = float(q) j = (mt.i - q) / (1 + q) mtj = Actuarial(nt=mt.nt, i=j) return ax(mtj, x, m)

geometrica def qaax(mt, x, q, m=1): """ geometrica """ q = float(q) j = (mt.i - q) / (1 + q) mtj = Actuarial(nt=mt.nt, i=j) return aax(mtj, x, m)

geometrica def qaxn(mt, x, n, q, m=1): """ geometrica """ q = float(q) j = (mt.i - q) / (1 + q) mtj = Actuarial(nt=mt.nt, i=j) return axn(mtj, x, n, m)

geometrica def qaaxn(mt, x, n, q, m = 1): """ geometrica """ #i = float(nt[1]) q = float(q) j = (mt.i - q) / (1 + q) mtj = Actuarial(nt=mt.nt, i=j) return aaxn(mtj, x, n, m)

geometrica def qtax(mt, x, t, q, m=1): """ geometrica """ q = float(q) j = (mt.i - q) / (1 + q) mtj = Actuarial(nt=mt.nt, i=j) return tax(mtj, x, t) + ((float(m - 1) / float(m * 2)) * (1 - nEx(mt, x, t)))

geometrica def qtaax(mt, x, t, q, m=1): """ geometrica """ q = float(q) j = (mt.i - q) / (1 + q) mtj = Actuarial(nt=mt.nt, i=j) return taax(mtj, x, t) - ((float(m - 1) / float(m * 2)) * (1 - nEx(mt, x, t)))